Contextualized and Localized Supplementary E-Learning Materials in Science 8 Physics

Evolutionary Design in Biological Physics and Materials Science

Active Learning in Materials Science and Engineering

Active learning is not only a key element of CDIO syllabi, but is expected to increase student motivation, commitment and retention. The incorporation of active learning elements into a materials engineering programme is considered by means of three example modules. The implications of introducing these modules into a materials programme is discussed in terms of five Ts – their titles, testing,...

Supplementary Materials: Self-Paced Learning with Diversity

This is the supplementary material for the paper entitled “Self-Paced Learning with Diversity”. The material is organized as follows: Section 1 gives the proof of Theorem 1. Section 2.1 and Section 2.2 present the detailed experimental settings and results on the MED (Multimedia Event Detection) dataset. Section 2.3 and Section 2.4 present the settings and detailed results on the Hollywood2 and...

Chapter 8 Computational Materials Science Research Team 8 . 1 Members

8.1 Members Seiji Yunoki (Team Leader) Yuichi Otsuka (Research Scientist) Shigetoshi Sota (Research Scientist) Shixun Zhang (Postdoctoral Researcher) Ahmad Ranjbar (Postdoctoral Researcher) James S. M. Anderson (Foreign Postdoctoral Researcher) Kazuhiro Seki (Special Postdoctoral Researcher, joint) Sandro Sorella (Senior Visiting Scientist) Takami Tohyama (Senior Visiting Scientist) Yutaka Imam...

Theory-Guided Machine Learning in Materials Science

Materials scientists are increasingly adopting the use of machine learning tools to discover hidden trends in data and make predictions. Applying concepts from data science without foreknowledge of their limitations and the unique qualities of materials data, however, could lead to errant conclusions. The differences that exist between various kinds of experimental and calculated data require c...